Abstract
Human faces play an important role in efficiently indexing and accessing video contents, especially broadcasting news video. However, face appearance in real environments exhibits many variations such as pose changes, facial expressions, aging, illumination changes, low resolution and occlusion, making it difficult for current state of the art face recognition techniques to obtain reasonable retrieval results. To handle this problem, this paper proposes an efficient retrieval method by integrating temporal information into facial intensity information. First, representative faces are quickly generated by using facial intensities to organize the face dataset into clusters. Next, temporal information is introduced to reorganize cluster memberships so as to improve overall retrieval performance. For scalability and efficiency, the clustering is based on a recently-proposed model involving correlations among relevant sets (neighborhoods) of data items. Neighborhood queries are handled using an approximate search index. Experiments on the 2005 TRECVID dataset show promising results.
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Le, DD., Satoh, S., Houle, M.E. (2006). Face Retrieval in Broadcasting News Video by Fusing Temporal and Intensity Information. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_40
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DOI: https://doi.org/10.1007/11788034_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36018-6
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